Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization

• Published in: Chemical engineering science 2018, Vol.175, p.12-24
• Main Authors: Haringa, Cees, Tang, Wenjun, Wang, Guan, Deshmukh, Amit T., van Winden, Wouter A., Chu, Ju, van Gulik, Walter M., Heijnen, Joseph J., Mudde, Robert F., Noorman, Henk J.
• Format: Article
• Language: English
• Subjects: CFD; Euler-Langrange; Industrial; Metabolic model; Scale-down
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2017.09.020

[Display omitted] •Metabolic-hydrodynamic simulation to predict yield loss due to substrate gradient.•Novel design approach for representative scale-down simulators.•Use of simulations to assess effect of reactor design on process yield.•Experimental verification of penicillin production rate in fed-batch simulation.•Prediction of emerging population heterogeneity in fed-batch simulation. We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.